Systems and Methods for Processing User Concentration Levels for Workflow Management

2. The system of claim 1, wherein the second user data comprises at least one of incoming content, an incoming message, or an incoming notification.

3. The system of claim 1, wherein the priority level of the second user data comprises at least one of an importance level, recency, or a relationship to other user data.

4. The system of claim 1, the meta-model comprises at least one of a neural network model, a deep-learning model, a convolutional neural network (CNN) model, a long short-term memory (LSTM) model or a random forest model.

5. The system of claim 1, wherein training the meta-model further comprises training the meta-model to determine a relationship between an email message and a user.

6. The system of claim 5, wherein the relationship between the email message and the user comprises a supervisory relationship or a subordinate relationship.

7. The system of claim 1, wherein generating the meta-model output comprises identifying a relationship between the feature data and the filtered first user data.

8. The system of claim 1, wherein the first user data comprises at least one of incoming message data, computer workflow data, or facial recognition image data.

9. The system of claim 1, wherein the filter model further comprises a data collection protocol, the data collection protocol including a data filter to reject or accept data.

10. The system of claim 9, wherein the data filter is based on the feature data.

11. The system of claim 1, wherein analyzing the priority level of the second user data further comprises analyzing incoming data using natural language processing.

13. The method of claim 12, wherein the feature model instance comprises at least one of an edge detection model or an objection detection model.

14. The method of claim 12, wherein updating the filter model comprises training the filter model based on a performance metric.

16. The non-transitory computer-readable medium of claim 15, wherein the feature data is associated with a feature model configured to generate a metric of priority.

17. The non-transitory computer-readable medium of claim 16, wherein the metric of priority comprises at least one of a delivery timestamp, an email address, or a subject line.

18. The non-transitory computer-readable medium of claim 16, wherein analyzing the priority of the second user data further comprises analyzing incoming data using natural language processing.